1. Field of the Invention
The present invention relates generally to surveillance systems, and more particularly to intelligent video surveillance systems.
2. Related Art
In a conventional intelligent video surveillance (IVS) system, automatic scene analysis is performed to extract and track all the possible surveillance targets in the camera field of view. The trajectories and behaviors of these targets are then analyzed, and alerts are sent once the trajectories and behaviors of the targets trigger user-defined rules.
As seen in FIG. 1, a conventional intelligent video surveillance (IVS) system may perform the processing steps of: background model maintenance 102, object detection 104, object tracking 106, and object classification 108. For further description of an exemplary IVS system. See, e.g., commonly owned U.S. patent application Ser. No. 09/987,707, which is incorporated herein by reference.
The background model maintenance step 102 monitors each pixel over time, remembering each pixel's typical appearance, and marks pixels different from the typical value as foreground. The object detection step 104 spatially groups these foreground pixels into foreground objects. Object tracking 106 connects these foreground objects temporally. Object classification 108 aims to categorize the tracked objects.
Referring now to step 102, there are at least two reasons that a pixel may be classified as foreground. First, the pixel could be part of a real moving object of interest (e.g. a person, a vehicle, or an animal). Second, the changes in the pixel could be caused by moving background (e.g. water, or foliage moving in the wind). Objects in the latter category, also called spurious foreground, can cause false alarms in the IVS system, thus detecting and eliminating these objects is very important. Certain spurious objects exhibit distinctly different motion and shape properties from real objects, and thus can be classified based on these properties as spurious. However, the motion and shape properties of other types of spurious objects may be very similar to those of real objects: they move consistently, without significant changes in size or shape.
Waves along the shoreline are a typical example of this behavior. As illustrated in FIGS. 2A-2C, a wave, for example, as seen in box 204, may be tracked consistently for several frames 202a, 202b, 202c, without significant change in size, shape or speed.
The performance of an IVS system is mainly measured by the detection rate and false alarm rate. A false alarm occurs when the IVS falsely identifies something in the video scene as being a target. In many cases, false alarms are triggered by spurious moving objects, such as, for example, waving tree branches, blowing leaves, and water ripples.
In video surveillance applications of scenes having a waterline, such as, for example, lakefront or beachfront, the tide is a large source of spurious objects that may trigger significant amount of false alarms.
What is needed then is an improved intelligent video surveillance system that overcomes shortcomings of conventional solutions.